This Rmarkdown file assesses the output of CheckV, DeepVirFinder, Kaiju, VIBRANT, VirSorter, and VirSorter2 on multiple training sets of microbial DNA, primarily from NCBI. Created from fungal, viral, bacterial, archeael, protist, and plasmid DNA sequences

Please reach out to James Riddell () or Bridget Hegarty () regarding any issues, or open an issue on github.

library(ggplot2)
There were 30 warnings (use warnings() to see them)
library(plyr)
library(reshape2)
library(viridis)
library(tidyr)
library(dplyr)
library(readr)
library(data.table)
library(pROC)
Type 'citation("pROC")' for a citation.

Attaching package: ‘pROC’

The following objects are masked from ‘package:stats’:

    cov, smooth, var
library("stringr")

Import the file that combines the results from each of the tools from running “combining_tool_output.Rmd”:

viruses <- read_tsv("../IntermediaryFiles/viral_tools_combined.tsv")

── Column specification ─────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  seqtype = col_character(),
  contig = col_character(),
  checkv_provirus = col_character(),
  checkv_quality = col_character(),
  method.x = col_character(),
  Classified = col_character(),
  IDs_all = col_character(),
  Seq = col_character(),
  Kaiju_Viral = col_character(),
  Kingdom = col_character(),
  type = col_character(),
  vibrant_quality = col_character(),
  method.y = col_character(),
  vibrant_prophage = col_character(),
  vs2type = col_character(),
  max_score_group = col_character(),
  provirus = col_logical()
)
ℹ Use `spec()` for the full column specifications.
There were 50 or more warnings (use warnings() to see the first 50)

This section defines a viralness score “keep_score” based on the tool classifications. A final keep_score above 1 indicates we will keep that sequence and call it viral.

VIBRANT Quality == “High Quality Draft”: +1 Quality == “Medium Quality Draft”: +1 Quality == “Low Quality Draft” & provirus: +0.5

Virsorter2 Viral >= 50: +0.5 Viral >= 0.95: +0.5 RNA >= 0.9: +1 lavidaviridae >= 0.9: +1 NCLDV >= 0.9: +1

Virsorter category == 1,4: +1 category == 2,5: +0.5

DeepVirFinder: Score >= 0.7: +0.5

Tuning - No Viral Signature: Kaiju_viral = “cellular organisms”: -0.5 If host_genes >50 and NOT provirus: -1 If viral_genes == 0 and host_genes >= 1: -1 If 3*viral_genes <= host_genes and NOT provirus: -1 If length > 50,000 and hallmark <=1: -1 If length < 5000 and checkv completeness <= 75: -0.5

Tuning - Viral Signature: Kaiju_viral = “Viruses”: +0.5 If %unknown >= 75 and length < 50000: +0.5 If %viral >= 50: +0.5 Hallmark > 2: +0.5

This script produces visualizations of these combined viral scorings and includes ecological metrics like alpha diversity.

You can decide which combination is appropriate for them and only need use the tools appropriate for your data.

getting_viral_set_1 <- function(input_seqs,
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                                include_vibrant=FALSE, 
                                include_virsorter2=FALSE,
                                include_deepvirfinder=FALSE,
                                include_tuning_viral=FALSE,
                                include_tuning_not_viral=FALSE,
                                include_virsorter=FALSE) {
  
  keep_score <- rep(0, nrow(input_seqs))
  
  if (include_vibrant) {
    keep_score[input_seqs$vibrant_quality=="high quality draft"] <- keep_score[input_seqs$vibrant_quality=="high quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="medium quality draft"] <- keep_score[input_seqs$vibrant_quality=="medium quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$provirus] <- keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$provirus] + 0.5
  }
  
  if (include_virsorter2) {
    #keep_score[input_seqs$viral>=50 | input_seqs$lavidaviridae>=0.95 | input_seqs$NCLDV>=0.95] <- keep_score[input_seqs$viral>=50 | input_seqs$lavidaviridae>=0.95 | input_seqs$NCLDV>=0.95] + 0.5
    keep_score[input_seqs$max_score>=50] <- keep_score[input_seqs$max_score>=50] + 0.5
    keep_score[input_seqs$max_score>=95] <- keep_score[input_seqs$max_score>=95] + 0.5
    #keep_score[input_seqs$RNA>=0.95] <- keep_score[input_seqs$RNA>=0.95] + 1
  }
  
  if (include_virsorter) {
    keep_score[input_seqs$category==1] <- keep_score[input_seqs$category==1] + 1
    keep_score[input_seqs$category==2] <- keep_score[input_seqs$category==2] + 0.5
    keep_score[input_seqs$category==4] <- keep_score[input_seqs$category==4] + 1
    keep_score[input_seqs$category==5] <- keep_score[input_seqs$category==5] + 0.5
  }
  
  if (include_deepvirfinder) {
    keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] + 0.5
    keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] + 0.5
  }
  
  if (include_tuning_viral) {
    keep_score[input_seqs$Kaiju_Viral=="Viruses"] <- keep_score[input_seqs$Kaiju_Viral=="Viruses"] + 0.5
    keep_score[input_seqs$hallmark>2] <- keep_score[input_seqs$hallmark>2] + 0.5
    keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] <- keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] + 0.5
    keep_score[input_seqs$viral>=50] <- keep_score[input_seqs$viral>=50] + 0.5
  }
  
  if (include_tuning_not_viral) {
    keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] <- keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] - 0.5
    keep_score[input_seqs$checkv_host_genes>50 & !input_seqs$provirus] <- keep_score[input_seqs$checkv_host_genes>50 & !input_seqs$provirus] - 1
    keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] <- keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] - 1
    keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & !input_seqs$provirus] <- keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & !input_seqs$provirus] - 1 
    keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] - 1
    keep_score[(input_seqs$checkv_completeness<=75 | input_seqs$vibrant_quality=="complete circular")& input_seqs$checkv_length<=5000] <- keep_score[input_seqs$checkv_completeness<=75 & input_seqs$checkv_length<=5000] - 0.5 
  }
  
  return(keep_score)
  
}

Assessing performance against the “truth”

note that this is only as accurate as the annotations of the input sequences

this function calculates the precision, recall, and F1 score for each pipeline

assess_performance <- function(seqtype, keep_score) {
  
  truepositive <- rep("not viral", length(seqtype))
  truepositive[seqtype=="virus"] <- "viral"
  
  #make confusion matrix
  confusion_matrix <- rep("true negative", length(keep_score))
  confusion_matrix[truepositive=="viral" & keep_score<=1] <- "false negative"
  confusion_matrix[truepositive=="viral" & keep_score>=1] <- "true positive"
  confusion_matrix[truepositive=="not viral" & keep_score>=1] <- "false positive"
  
  TP <- table(confusion_matrix)[4]
  FP <- table(confusion_matrix)[2]
  TN <- table(confusion_matrix)[3]
  FN <- table(confusion_matrix)[1]
  
  precision <- TP/(TP+FP)
  recall <- TP/(TP+FN)
  F1 <- 2*precision*recall/(precision+recall)
  
  MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
  
  auc <- round(auc(truepositive, keep_score),4)
  
  #by type metrics
  fungal_FP <- table(confusion_matrix[seqtype=="fungi"])[2]
  protist_FP <- table(confusion_matrix[seqtype=="protist"])[2]
  bacterial_FP <- table(confusion_matrix[seqtype=="bacteria"])[2]
  viral_FN <- table(confusion_matrix[seqtype=="virus"])[1]
  
  performance <- c(precision, recall, F1, MCC, auc, fungal_FP, 
                   protist_FP, bacterial_FP, viral_FN)
  names(performance) <- c("precision", "recall", "F1", "MCC", "AUC", "fungal_FP",
                          "protist_FP", "bacterial_FP", "viral_FN")
  
  return(performance)
}

combination of tools list

combos_list <- data.frame(toolcombo=rep(0, 64),
There were 27 warnings (use warnings() to see them)
                          tune_not_viral=rep(0, 64),
                          DVF=rep(0, 64),
                          tune_viral=rep(0, 64),
                          VIBRANT=rep(0, 64),
                          VS=rep(0, 64),
                          VS2=rep(0, 64))
p <- 1

for (i in c(0,1)){
  for (j in c(0,1)){
    for (k in c(0,1)){
      for (l in c(0,1)){
        for (m in c(0,1)){
          for (n in c(0,1)){
            combos_list$toolcombo[p] <- paste(i,j,k,l,m,n)
            combos_list$toolcombo2[p] <- paste(if(i){"tv"}else{"0"},if(j){"DVF"}else{"0"},
                                               if(k){"tnv"}else{"0"},if(l){"VB"}else{"0"},
                                               if(m){"VS"}else{"0"},if(n){"VS2"}else{"0"})
            combos_list$tune_not_viral[p] <- i
            combos_list$DVF[p] <- j
            combos_list$tune_viral[p] <- k
            combos_list$VIBRANT[p] <- l
            combos_list$VS[p] <- m
            combos_list$VS2[p] <- n
            p <- p+1
          }
        }
      }
    }
  }
}

combos_list <- combos_list[-1,]

this function builds a list of all of the combinations that the user wants to test. In this case, we’re comparing the performance of all unique combinations of the six tools.

build_score_list <- function(input_seqs, combos) {
  output <- data.frame(precision=rep(0, nrow(combos)),
                       recall=rep(0, nrow(combos)),
                       F1=rep(0, nrow(combos)),
                       MCC=rep(0, nrow(combos)),
                       AUC=rep(0, nrow(combos)),
                       fungal_FP=rep(0, nrow(combos)),
                       protist_FP=rep(0, nrow(combos)),
                       bacterial_FP=rep(0, nrow(combos)),
                       viral_FN=rep(0, nrow(combos)))
  for (i in 1:nrow(combos)) {
    keep_score <- getting_viral_set_1(input_seqs, include_vibrant = combos$VIBRANT[i],
                                            include_virsorter = combos$VS[i],
                                            include_virsorter2 = combos$VS2[i],
                                            include_tuning_viral = combos$tune_viral[i],
                                            include_tuning_not_viral = combos$tune_not_viral[i],
                                            include_deepvirfinder = combos$DVF[i])
  
    output[i,1:9] <- assess_performance(input_seqs$seqtype, keep_score)
    
    output$toolcombo[i] <- paste(combos$tune_viral[i],combos$DVF[i],
                                 combos$tune_not_viral[i], combos$VIBRANT[i],
                                 combos$VS[i], combos$VS2[i])
  }
  
  output[is.na(output)] <- 0

  return (output)
}

Calculate the performance of each pipeline

accuracy_scores <- data.frame(testing_set_index=rep(0, nrow(combos_list)*10),
                      precision=rep(0, nrow(combos_list)*10),
                       recall=rep(0, nrow(combos_list)*10),
                       F1=rep(0, nrow(combos_list)*10),
                       MCC=rep(0, nrow(combos_list)*10), 
                      AUC=rep(0, nrow(combos_list)*10),
                      fungal_FP=rep(0, nrow(combos_list)*10),
                      protist_FP=rep(0, nrow(combos_list)*10),
                      bacterial_FP=rep(0, nrow(combos_list)*10),
                      viral_FN=rep(0, nrow(combos_list)*10))

accuracy_scores <- cbind(testing_set_index=rep(1, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==1,], combos_list))
for (i in 2:10) {
  accuracy_scores <- rbind(accuracy_scores,
                           cbind(testing_set_index=rep(i, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==i,], combos_list)))
}
accuracy_scores$numrules <- str_count(accuracy_scores$toolcombo, "1")
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#accuracy_scores <- accuracy_scores[order(accuracy_scores$numrules, decreasing=F),]
accuracy_scores <- accuracy_scores[order(accuracy_scores$MCC, decreasing=F),]
accuracy_scores$toolcombo <- factor(accuracy_scores$toolcombo, levels = unique(accuracy_scores$toolcombo))
accuracy_scores$numrules <- as.factor(accuracy_scores$numrules)
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
There were 27 warnings (use warnings() to see them)

Visualize how the precision, recall, and F1 scores change across pipelines.

p2 <- ggplot(accuracy_scores, aes(x=toolcombo, y=F1, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("F1 Score")
p2


ggplot(accuracy_scores, aes(x=toolcombo, y=precision, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Precision")

ggplot(accuracy_scores, aes(x=toolcombo, y=recall, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Recall")


ggplot(accuracy_scores, aes(x=precision, y=recall, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=20),
  ) +
  xlab("Precision") +
  ylab("Recall") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1))


ggplot(accuracy_scores[accuracy_scores$testing_set_index==1,], aes(x=precision, y=recall)) +
  geom_label(alpha=0.7, label=accuracy_scores$toolcombo[accuracy_scores$testing_set_index==1]) +
  geom_point(alpha=0.5, aes(color=numrules, fill=numrules)) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Precision") +
  ylab("Recall")


ggplot(accuracy_scores, aes(x=toolcombo, y=abs(precision-recall), 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Precision-Recall")

ggplot(accuracy_scores, aes(x=toolcombo, y=MCC, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=20),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("MCC") +
  scale_fill_manual(name="Number of Rule Sets",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="Number of Rule Sets",
                     values = alpha(rev(pal(6)), 1))

  
ggplot(accuracy_scores, aes(x=toolcombo, y=AUC, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("AUC")

ggplot(accuracy_scores, aes(x=toolcombo, y=fungal_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Fungal False Positives")


ggplot(accuracy_scores, aes(x=toolcombo, y=protist_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Protist False Positives")


ggplot(accuracy_scores, aes(x=toolcombo, y=bacterial_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Bacterial False Positives")


ggplot(accuracy_scores, aes(x=toolcombo, y=viral_FN, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Viral False Negatives")


ggplot(accuracy_scores, aes(x=toolcombo, y=viral_FN, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Viral False Negatives")


ggplot(accuracy_scores, aes(x=protist_FP, y=fungal_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Protist FP") +
  ylab("Fungal FP")


ggplot(accuracy_scores, aes(x=recall, y=fungal_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Recall") +
  ylab("Fungal FP")


ggplot(accuracy_scores, aes(x=recall, y=protist_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Recall") +
  ylab("Protist FP")

accuracy_scores_melt <- accuracy_scores %>% 
There were 50 or more warnings (use warnings() to see the first 50)
  select(testing_set_index, precision, recall, MCC, numrules, toolcombo) %>%
  pivot_longer(cols=c(precision, recall, MCC), 
               names_to="performance_metric",
               values_to="performance_metric_score")
ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric=="MCC",], aes(x=numrules, y=performance_metric_score, 
There were 30 warnings (use warnings() to see them)
                                  color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Number of Rule Sets") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) 

NA
ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric=="recall",], aes(x=numrules, y=performance_metric_score, 
There were 30 warnings (use warnings() to see them)
                                  color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("recall") +
  xlab("Number of Rule Sets") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) 

NA
ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric!="MCC",], aes(x=numrules, y=performance_metric_score, 
                                  color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("Score") +
  xlab("Number of Rule Sets") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  facet_wrap(~performance_metric)

NA

comparing metric with and without tuning rules

accuracy_scores_melt$tuning_inc <- "no"
There were 50 or more warnings (use warnings() to see the first 50)
accuracy_scores_melt$tuning_inc[substring(accuracy_scores_melt$toolcombo, 1, 1)==1] <- "tv"
accuracy_scores_melt$tuning_inc[substring(accuracy_scores_melt$toolcombo, 3, 3)==1] <- "tnv"
accuracy_scores_melt$tuning_inc[substring(accuracy_scores_melt$toolcombo, 1, 1)==1 &
                                  substring(accuracy_scores_melt$toolcombo, 3, 3)==1] <- "both"
ggplot(accuracy_scores_melt, aes(x=tuning_inc, y=performance_metric_score)) +
  geom_boxplot() +
  geom_point(aes(color=numrules, fill=numrules), alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Number of Tools") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  facet_wrap(~performance_metric)


ggplot(accuracy_scores_melt, aes(x=tuning_inc, y=performance_metric_score,
                                 color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Number of Tools") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  facet_wrap(~performance_metric)


ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric!="MCC",], aes(x=tuning_inc, y=performance_metric_score)) +
  geom_boxplot() +
  geom_boxplot(aes(color=numrules, fill=numrules)) +
 # geom_point(aes(color=numrules, fill=numrules), alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("Score") +
  xlab("") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.3)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 0.7)) +
  facet_wrap(~performance_metric)

NA
write_tsv(accuracy_scores, "20221029_accuracy_scores.tsv")
There were 50 or more warnings (use warnings() to see the first 50)

to do: add in clustering and ordination like in the drinking water R notebook

Experimenting

high precision example

viruses$keep_score_high_precision <- getting_viral_set_1(viruses, include_deepvirfinder = T,
There were 20 warnings (use warnings() to see them)
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = T,
                                              include_virsorter = F)
viruses$confusion_matrix_high_precision <- "true negative"
There were 25 warnings (use warnings() to see them)
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision<1] <- "false negative"
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision>=1] <- "true positive"
viruses$confusion_matrix_high_precision[viruses$seqtype!="virus" & viruses$keep_score_high_precision>=1] <- "false positive"

visualizing confusion matrix by taxa

confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype, viruses$Index))
The melt generic in data.table has been passed a table and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype,     viruses$Index)). In the next version, this warning will become an error.
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
length(grep("true", viruses$confusion_matrix_high_precision))/nrow(viruses)
[1] 0.9206364
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
There were 44 warnings (use warnings() to see them)
ggplot(confusion_by_taxa, aes(y=count, x=confusion_matrix,
There were 44 warnings (use warnings() to see them)
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free")

this rule set had the highest precision, but as you can see, this comes with a big sacrifice in recall

high MCC example

viruses$keep_score_high_MCC <- getting_viral_set_1(viruses, include_deepvirfinder = F,
There were 50 or more warnings (use warnings() to see the first 50)
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)
viruses$confusion_matrix_high_MCC <- "true negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC<1] <- "false negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC>=1] <- "true positive"
viruses$confusion_matrix_high_MCC[viruses$seqtype!="virus" & viruses$keep_score_high_MCC>=1] <- "false positive"

accuracy:

length(grep("true", viruses$confusion_matrix_high_MCC))/nrow(viruses)
[1] 0.935778

recall

length(grep("true positive", viruses$confusion_matrix_high_MCC))/length(grep("virus", viruses$seqtype))
[1] 0.7078
There were 24 warnings (use warnings() to see them)
TP <- table(viruses$confusion_matrix_high_MCC)[4]
FP <- table(viruses$confusion_matrix_high_MCC)[2]
TN <- table(viruses$confusion_matrix_high_MCC)[3]
FN <- table(viruses$confusion_matrix_high_MCC)[1]

precision <- as.numeric(TP/(TP+FP))
precision
[1] 0.6845261
recall <- as.numeric(TP/(TP+FN))
recall
[1] 0.7078
F1 <- as.numeric(2*precision*recall/(precision+recall))
F1
[1] 0.6959685
MCC <- as.numeric((TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN)))
MCC
[1] 0.6601921

precision=69%, recall=87%, MCC=77%

precision adjusting size to be equal viral/not viral

TP <- table(viruses$confusion_matrix_high_MCC)[4]
There were 48 warnings (use warnings() to see them)
FP <- table(viruses$confusion_matrix_high_MCC)[2]*.11
TN <- table(viruses$confusion_matrix_high_MCC)[3]*.11
FN <- table(viruses$confusion_matrix_high_MCC)[1]

precision <- as.numeric(TP/(TP+FP))
precision
[1] 0.9561163
recall <- as.numeric(TP/(TP+FN))
recall
[1] 0.7523
F1 <- as.numeric(2*precision*recall/(precision+recall))
F1
[1] 0.8420504
MCC <- as.numeric((TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN)))
MCC
[1] 0.7293445

precision=0.95, recall=0.87, F1=0.91, MCC=0.82

visualizing confusion matrix by taxa

confusion_by_taxa <- viruses %>% count(confusion_matrix_high_MCC, seqtype, Index)
There were 24 warnings (use warnings() to see them)
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","index", "count")
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(index),
There were 24 warnings (use warnings() to see them)
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

differences based on genome size

viruses$size_class <- "3-5kb"
viruses$size_class[viruses$checkv_length>5000] <- "5-10kb"
viruses$size_class[viruses$checkv_length>10000] <- ">10kb"
confusion_by_taxa <- viruses %>% count(confusion_matrix_high_MCC, seqtype, size_class, Index)

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","size", "index", "count")
confusion_vir_called <- confusion_by_taxa %>% filter(confusion_matrix=="true positive" | confusion_matrix=="false positive") 
There were 20 warnings (use warnings() to see them)
type_count <- viruses %>% count(seqtype, size_class, Index)

confusion_vir_called$per_viral <- 0

for (i in c(1:nrow(confusion_vir_called))) {
  confusion_vir_called$per_viral[i] <- confusion_vir_called$count[i]/type_count$n[type_count$seqtype==confusion_vir_called$seqtype[i] & 
                                                                                    type_count$Index==confusion_vir_called$index[i] &
                                                                                    type_count$size_class==confusion_vir_called$size[i]]*100
}

confusion_vir_called <- confusion_vir_called %>% group_by(seqtype, size) %>%
  summarise(mean=mean(per_viral), 
            sd=sd(per_viral))
`summarise()` has grouped output by 'seqtype'. You can override using the `.groups` argument.
confusion_vir_called$size <- factor(confusion_vir_called$size,
                                    levels = c("3-5kb", "5-10kb", ">10kb"))
ggplot(confusion_vir_called, aes(y=mean, x=size,
                   fill=seqtype,
                   color=seqtype)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,
                 position=position_dodge(.9)) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Length") +
  ylab("Sequences Called Viral (%)") 

viruses$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
There were 23 warnings (use warnings() to see them)
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf_vs2 <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf_vs2_vs <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses$keep_score_vb_dvf_vs2_vs_tv <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses$keep_score_vb_dvf_vs2_vs_tv_tnv <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)
viruses$true_virus <- "not"
viruses$true_virus[viruses$seqtype=="virus"] <- "virus"

viruses_long_scores <- viruses %>% 
  select(contains("keep_score_vb"), size_class, true_virus) %>%
  pivot_longer(cols=contains("keep_score_"), 
               names_to="rule_combination",
               values_to="viral_score") %>% 
  mutate(viral_score=as.factor(round(viral_score))) %>%
  group_by(rule_combination, viral_score, size_class, true_virus) %>%
  summarise(n = n())
`summarise()` has grouped output by 'rule_combination', 'viral_score', 'size_class'. You can override using the `.groups` argument.
viruses_long_scores$size_class <- factor(viruses_long_scores$size_class,
                                    levels = c("3-5kb", "5-10kb", ">10kb"))
viruses_long_scores_addition <- viruses_long_scores[(viruses_long_scores$true_virus=="virus" &  (viruses_long_scores$rule_combination!="keep_score_vb_dvf_vs2_vs_tv_tnv") & viruses_long_scores$viral_score!="0"),]
There were 23 warnings (use warnings() to see them)
ggplot(viruses_long_scores_addition, aes(y=n, x=rule_combination,
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "Purples") +
  xlab("") +
  ylab("Number of Sequences") + 
  scale_x_discrete(labels=c("VB", "VB+DVF", "VB+DVF+VS2", "VB+DVF+VS2+VS",
                            "VB+DVF+VS2+VS+addition")) +
  facet_grid(~true_virus, scales = "free")

ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
There were 50 or more warnings (use warnings() to see the first 50)
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  scale_x_discrete(labels=c("VB", "VB+DVF", "VB+DVF+VS2", "VB+DVF+VS2+VS",
                            "VB+DVF+VS2+VS+addition", "VB+DVF+VS2+VS+addition-removal")) +
  facet_grid(~true_virus, scales = "free")

ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
There were 20 warnings (use warnings() to see them)
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  facet_grid(size_class~true_virus, scales = "free")

Considering how each method contributes to the final prediction

viruses_high <- viruses[viruses$keep_score_vb_dvf_vs2_vs_tv>=1,] 
There were 50 or more warnings (use warnings() to see the first 50)
viruses_high_mod <- viruses_high %>% select(keep_score_vb,keep_score_vb_dvf, 
                                            keep_score_vb_dvf_vs2, keep_score_vb_dvf_vs2_vs, 
                                            keep_score_vb_dvf_vs2_vs_tv, keep_score_vb_dvf_vs2_vs_tv_tnv)
#viruses_high_mod <- apply(viruses_high_mod, c(1,2), function(x) {if (x >= 1) {x <- 1} else {x <- 0}})
viruses_high_mod <- as_tibble(viruses_high_mod)
sm_m <- reshape2::melt(viruses_high_mod)
No id variables; using all as measure variables
colnames(sm_m) <- c("method", "viral_score")
sm_m <- sm_m[sm_m$viral_score>0,]

sm_m$score <- sm_m$viral_score

sm_m$score[sm_m$viral_score==0.5] <- "0.5"
sm_m$score[sm_m$viral_score>=1] <- "1"
sm_m$score[sm_m$viral_score>=2] <- "2"
sm_m$score[sm_m$viral_score>=3] <- "3"
sm_m$score[sm_m$viral_score>=4] <- "4"
sm_m$score[sm_m$viral_score>=5] <- "5"

sm_m$score <- factor(sm_m$score, 
                                       levels=c("0.5", "1", "2","3","4","5"))
ggplot(sm_m, aes(x=method, y=score,
                   fill=score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 1)) +
  xlab("") +
  ylab("Number of Sequences") +
  coord_flip()
Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Another way of visualizing between rule sets

viruses_mcc_alluvial <- data.frame(seqtype=viruses$seqtype,
                                   keep_score_high_MCC=viruses$keep_score_high_MCC,
                                   confusion_matrix_high_MCC=viruses$confusion_matrix_high_MCC)


viruses_mcc_alluvial$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_dvf <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_vs <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = F,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses_mcc_alluvial$keep_score_vs2 <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = F,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_tv <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = F,
                                              include_virsorter2 = F,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_tnv <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = T,
                                              include_virsorter = F)
viruses_mcc_alluvial %>%
  count(seqtype, keep_score_high_MCC) %>% 
  spread(key = keep_score_high_MCC, value=n)
viruses_mcc_alluvial <- viruses_mcc_alluvial %>%
  count(seqtype, keep_score_dvf, keep_score_vb, keep_score_vs,
        keep_score_vs2, keep_score_tv, keep_score_tnv, keep_score_high_MCC) %>%
  mutate(high_mcc_viral_score=factor(round(keep_score_high_MCC)))
ggplot(viruses_mcc_alluvial,
       aes(axis1 = keep_score_dvf, axis2 = keep_score_vb, 
           axis3 = keep_score_vs, axis4 = keep_score_vs2, 
           axis5 = keep_score_tv, axis6 = keep_score_tnv, 
           y=n)) +
  geom_alluvium(aes(fill=high_mcc_viral_score),
                width = 0, knot.pos = 0, reverse = FALSE) +
  geom_stratum(width = 1/5) +
  theme_bw() +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)),
            reverse = FALSE) +
  theme(
        axis.text.x=element_text(size=14, angle = 90)
        ) +
  scale_x_continuous(breaks=c(1,2,3,4,5,6),
    labels=c("dvf", "kj", "vs", "vs2",
             "tv", "tnv")) +
  scale_fill_brewer(palette = "PuOr", ) +
  facet_wrap(~seqtype, scales="free_y") 

Visualizing confusion matrix by number of tools

viruses$keep_score_visualize <- viruses$keep_score_high_MCC
viruses$keep_score_visualize[viruses$keep_score_high_MCC>1] <- "> 1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==1] <- "1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==0.5] <- "0.5"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==0] <- "0"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==-0.5] <- "-0.5"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==-1] <- "-1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC<=-1] <- "< -1"

viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
                                       levels=c("< -1", "-1", "-0.5", "0", "0.5","1", "> 1"))
#viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
#                                       labels=c("≤ 0", "≤ 0", "≤ 0", "0.5","1", "> 1"))
levels(factor(viruses$keep_score_visualize))
ggplot(viruses, aes(x=as.factor(Index),
                   fill=keep_score_visualize, color=keep_score_visualize)) +
  geom_bar(stat="count", position="stack") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16)
  ) +
  scale_color_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 1)) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 0.5)) +
  xlab("Index") +
  ylab("Sequence Count") +
  facet_wrap(~confusion_matrix_high_MCC, scales = "free")

clustering

viral_scores <- matrix(data=0, nrow=nrow(viruses), ncol=nrow(combos_list))
num_viruses <- data.frame(toolcombo=rep(0, nrow(combos_list)),
                          num_viruses=rep(0, nrow(combos_list)))

for (i in 1:nrow(combos_list)) {
  viral_scores[,i] <- getting_viral_set_1(viruses, include_vibrant = combos_list$VIBRANT[i],
                                            include_virsorter = combos_list$VS[i],
                                            include_virsorter2 = combos_list$VS2[i],
                                            include_tuning_viral = combos_list$tune_viral[i],
                                            include_tuning_not_viral = combos_list$tune_not_viral[i],
                                            include_deepvirfinder = combos_list$DVF[i])
  
  if (max(viral_scores[,i])<=0) {
    num_viruses$num_viruses[i] <- 0
  }
  else {
    num_viruses$num_viruses[i] <- table(viral_scores[,i]>=1)[[2]]
  }
  
  num_viruses$toolcombo[i] <- combos_list$toolcombo[i]
  
  num_viruses$toolcombo2[i] <- combos_list$toolcombo2[i]
}

num_viruses$numrules <- str_count(num_viruses$toolcombo, "1")
num_viruses <- num_viruses[order(num_viruses$num_viruses, decreasing=F),]
num_viruses$toolcombo <- factor(num_viruses$toolcombo, levels = unique(num_viruses$toolcombo))
num_viruses$toolcombo2 <- factor(num_viruses$toolcombo2, levels = unique(num_viruses$toolcombo2))
num_viruses$numrules <- as.factor(num_viruses$numrules)
viral_scores_nozeros <- viral_scores[rowSums(viral_scores)>0,]
viral_scores_nozeros <- viral_scores_nozeros + 1
viral_scores_nozeros <- as.data.frame(viral_scores_nozeros)

colnames(viral_scores_nozeros) <- num_viruses$toolcombo
library(phyloseq)
tooldata <- num_viruses

rownames(tooldata) <- tooldata$toolcombo
physeq_pooled <- phyloseq(otu_table(viral_scores_nozeros, taxa_are_rows = T),
                                     sample_data(tooldata))
ordination <- phyloseq::ordinate(physeq =physeq_pooled, method = "PCoA", distance = "bray")
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw() +
  geom_label(label=tooldata$toolcombo)

phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw()
bray_dist <- phyloseq::distance(physeq_pooled, method="bray")
clusters <- hclust(dist(bray_dist))
plot(clusters)

myclusters <- cutree(clusters, h=0.8)
#names(myclusters[myclusters==1])
#names(myclusters[myclusters==2])
#names(myclusters[myclusters==3])
#names(myclusters[myclusters==4])
#names(myclusters[myclusters==5])

myclusters_df <- tibble(combo=names(myclusters),
                            cluster_index=myclusters)

myclusters_df <- separate(myclusters_df, col=combo, into=c("tnv", "DVF",
                                                            "tv", "VB",
                                                            "VS", "VS2"),
                          sep=" ", remove = F)


tool_count <- as.data.frame(rbind(table(myclusters_df$tnv, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$DVF, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$tv, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VB, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VS, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VS2, myclusters_df$cluster_index)[2,])
                    )

tool_count <- data.frame(t(apply(tool_count, c(1), function(x) {x <- x/table(myclusters_df$cluster_index)})))



tool_count$method <- c("tnv", "DVF", "tv", "VB", "VS", "VS2")

tool_count <- melt(tool_count)

colnames(tool_count) <- c("tool", "cluster_index", "tool_count_norm")
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

ggplot(tool_count, aes(x=tool, y=tool_count_norm,
                   fill=tool,
                   color=tool)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "none",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    #legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Tool") +
  ylab("Proportion of Times in Cluster") + 
  facet_wrap(~cluster_index, nrow=1)
accuracy_scores_melt <- accuracy_scores %>% 
  select(precision, recall, MCC, numrules, toolcombo) %>%
  group_by(numrules, toolcombo) %>%
  summarise(precision=mean(precision),
            recall=mean(recall),
            MCC=mean(MCC)) %>%
  pivot_longer(cols=c(precision, recall, MCC), 
               names_to="performance_metric",
               values_to="performance_metric_score")
myclusters_df <- inner_join(accuracy_scores_melt, myclusters_df, 
                            by=c("toolcombo"="combo"))

myclusters_df$cluster_index <- as.factor(myclusters_df$cluster_index)
ggplot(myclusters_df, aes(x=cluster_index, y=performance_metric_score, 
                                  color=cluster_index, fill=cluster_index)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Cluster") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(9)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(9)), 1)) +
  facet_wrap(~performance_metric)

all 6 tools example

viruses$keep_score_all <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)
viruses$confusion_matrix_all <- "true negative"
viruses$confusion_matrix_all[viruses$seqtype=="virus" & viruses$keep_score_all<1] <- "false negative"
viruses$confusion_matrix_all[viruses$seqtype=="virus" & viruses$keep_score_all>=1] <- "true positive"
viruses$confusion_matrix_all[viruses$seqtype!="virus" & viruses$keep_score_all>=1] <- "false positive"
TP <- table(viruses$confusion_matrix_all)[4]
FP <- table(viruses$confusion_matrix_all)[2]
TN <- table(viruses$confusion_matrix_all)[3]
FN <- table(viruses$confusion_matrix_all)[1]

precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
F1 <- 2*precision*recall/(precision+recall)
F1

MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
MCC

precision=62%, recall=92%, MCC=73%

visualizing confusion matrix by taxa

confusion_by_taxa <- melt(table(viruses$confusion_matrix_all, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
table(viruses$confusion_matrix_all)

length(grep("true", viruses$confusion_matrix_all))/nrow(viruses)
length(grep("true positive", viruses$confusion_matrix_all))/length(grep("virus", viruses$seqtype))
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

high recall example

viruses$keep_score_high_recall <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)
viruses$confusion_matrix_high_recall <- "true negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall<1] <- "false negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall>=1] <- "true positive"
viruses$confusion_matrix_high_recall[viruses$seqtype!="virus" & viruses$keep_score_high_recall>=1] <- "false positive"

visualizing confusion matrix by taxa

confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_recall, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
p2 <- ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
p2

accuracy:

length(grep("true", viruses$confusion_matrix_high_recall))/nrow(viruses)

0.887

recall

length(grep("true positive", viruses$confusion_matrix_high_recall))/length(grep("virus", viruses$seqtype))

recover almost all of the viruses this way, but more protist contamination

0.960

confusion_by_taxa <- viruses %>% count(confusion_matrix_high_recall, seqtype, size_class)

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","size", "count")

few tools, high MCC example

viruses$keep_score_few_tools <- getting_viral_set_1(viruses, include_deepvirfinder = F,
There were 50 or more warnings (use warnings() to see the first 50)
                                              include_vibrant = F,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = F)
viruses$confusion_matrix_few_tools <- "true negative"
viruses$confusion_matrix_few_tools[viruses$seqtype=="virus" & viruses$keep_score_few_tools<1] <- "false negative"
viruses$confusion_matrix_few_tools[viruses$seqtype=="virus" & viruses$keep_score_few_tools>=1] <- "true positive"
viruses$confusion_matrix_few_tools[viruses$seqtype!="virus" & viruses$keep_score_few_tools>=1] <- "false positive"
TP <- table(viruses$confusion_matrix_few_tools)[4]
FP <- table(viruses$confusion_matrix_few_tools)[2]
TN <- table(viruses$confusion_matrix_few_tools)[3]
FN <- table(viruses$confusion_matrix_few_tools)[1]

precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
F1 <- 2*precision*recall/(precision+recall)
F1

MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
MCC

precision=77%, recall=76%, MCC=74%

Visualizing the different sets

confusion_by_taxa_method <- viruses %>% 
  select(contains("confusion_matrix"), seqtype, Index) %>%
  pivot_longer(cols=contains("confusion_matrix"), 
               names_to="confusion_matrix_type",
               values_to="confusion_matrix_value") %>%
  count(seqtype, Index, confusion_matrix_type, confusion_matrix_value)
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
ggplot(confusion_by_taxa_method, aes(y=n, x=confusion_matrix_type,
                   fill=confusion_matrix_value,
                   color=confusion_matrix_value)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free")
confusion_by_taxa_method <- viruses %>% 
  select(contains("confusion_matrix"), seqtype, Index) %>%
  pivot_longer(cols=contains("confusion_matrix"), 
               names_to="confusion_matrix_type",
               values_to="confusion_matrix_value") %>%
  count(seqtype, Index, confusion_matrix_type, confusion_matrix_value) %>%
  filter(grepl("true", confusion_matrix_value)) %>%
  mutate(confusion_matrix_type=sub("confusion_matrix_", "", confusion_matrix_type))
ggplot(confusion_by_taxa_method, aes(y=n, x=confusion_matrix_type,
                   fill=confusion_matrix_value,
                   color=confusion_matrix_value)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=10, angle=90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4))[3:4], 0.5),
                    labels=c( 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4))[3:4], 1),
                    labels=c(
                             "true negative", "true positive")) +
  xlab("Tool Set") +
  ylab("Contig Count") + 
  facet_wrap(~seqtype, scales = "free")

another way of visualizing the different tool sets scores

viruses$true_virus <- "not"
There were 40 warnings (use warnings() to see them)
viruses$true_virus[viruses$seqtype=="virus"] <- "virus"

viruses_long_scores <- viruses %>% 
  select(contains("keep_score_high"), contains("keep_score_all"), size_class, true_virus) %>%
  pivot_longer(cols=contains("keep_score_"), 
               names_to="rule_combination",
               values_to="viral_score") %>% 
  mutate(viral_score=as.factor(round(viral_score))) %>%
  group_by(rule_combination, viral_score, size_class, true_virus) %>%
  summarise(n = n())
`summarise()` has grouped output by 'rule_combination', 'viral_score', 'size_class'. You can override using the `.groups` argument.
viruses_long_scores$size_class <- factor(viruses_long_scores$size_class,
                                    levels = c("3-5kb", "5-10kb", ">10kb"))
ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
There were 20 warnings (use warnings() to see them)
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  facet_grid(~true_virus, scales = "free")

ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
There were 20 warnings (use warnings() to see them)
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  facet_grid(size_class~true_virus, scales = "free")

Extra Stuff #####################################################################

ggplot(viruses, aes(x=checkv_length, y=keep_score_high_MCC,
                   fill=confusion_matrix_high_MCC,
                   color=confusion_matrix_high_MCC)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
ggplot(viruses, aes(x=checkv_completeness, y=hallmark,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Number of Hallmark Genes") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
ggplot(viruses, aes(x=checkv_completeness, y=keep_score_high_MCC,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
ggplot(viruses, aes(x=confusion_matrix_high_recall, y=checkv_length,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") +
  scale_y_log10()

looking at false negatives

viruses_false_negs <- viruses[(viruses$seqtype=="virus" & viruses$keep_score_high_recall<1),]

looking at protists calling viral

viruses_false_pos_protists <- viruses[(viruses$seqtype=="protist" & viruses$keep_score_high_recall>=1),]

Considering how each method contributes to the final prediction (high MCC)

viruses$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_tv <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)
viruses_high <- viruses[viruses$keep_score_vb_tv>=1,] #uncomment this line if want to use all 6 tools
viruses_high_mod <- viruses_high %>% select(keep_score_vb, 
                                            keep_score_vb_tv)
#viruses_high_mod <- apply(viruses_high_mod, c(1,2), function(x) {if (x >= 1) {x <- 1} else {x <- 0}})
viruses_high_mod <- as_tibble(viruses_high_mod)
sm_m <- reshape2::melt(viruses_high_mod)
colnames(sm_m) <- c("method", "score")
ggplot(sm_m, aes(x=method, y=score,
                   fill=as.factor(score))) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_manual(name = 'Number of Methods',
                     values = alpha(c(viridis(14)), 1)) +
  xlab("Primary Method") +
  ylab("Count of Viral Contigs") +
  coord_flip()

ROC

library(pROC)
viruses$truepositive <- rep(0, nrow(viruses))
viruses$truepositive[viruses$seqtype=="virus"] <- 1
rocobj <- roc(viruses$truepositive, viruses$keep_score)
rocobj_all <- roc(viruses$truepositive, viruses$keep_score_all)
auc <- round(auc(viruses$truepositive, viruses$keep_score),4)
auc_all <- round(auc(viruses$truepositive, viruses$keep_score_all),4)
#create ROC plot
ggroc(rocobj, colour = 'steelblue', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc, ')')) +
  coord_equal()
ggroc(rocobj_all, colour = 'green', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc_all, ')'))

Sensitivity: The probability that the model predicts a positive outcome for an observation when indeed the outcome is positive. Specificity: The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative.

Comparing behavior of all testing sets combined (clustering analyses)

viral_scores <- matrix(data=0, nrow=nrow(viruses), ncol=nrow(combos_list))
num_viruses <- data.frame(toolcombo=rep(0, nrow(combos_list)),
                          num_viruses=rep(0, nrow(combos_list)))

for (i in 1:nrow(combos_list)) {
  viral_scores[,i] <- getting_viral_set_1(viruses, include_vibrant = combos_list$VIBRANT[i],
                                            include_virsorter = combos_list$VS[i],
                                            include_virsorter2 = combos_list$VS2[i],
                                            include_tuning = combos_list$CheckV[i],
                                            include_kaiju = combos_list$Kaiju[i],
                                            include_deepvirfinder = combos_list$DVF[i])
  
  num_viruses$num_viruses[i] <- table(viral_scores[,i]>=1)[[2]]
  
  num_viruses$toolcombo[i] <- combos_list$toolcombo[i]
  
  num_viruses$toolcombo2[i] <- combos_list$toolcombo2[i]
}

num_viruses$numrules <- str_count(num_viruses$toolcombo, "1")
num_viruses <- num_viruses[order(num_viruses$num_viruses, decreasing=F),]
num_viruses$toolcombo <- factor(num_viruses$toolcombo, levels = unique(num_viruses$toolcombo))
num_viruses$toolcombo2 <- factor(num_viruses$toolcombo2, levels = unique(num_viruses$toolcombo2))
num_viruses$numrules <- as.factor(num_viruses$numrules)
ggplot(num_viruses, aes(x=toolcombo, y=num_viruses, 
                                  color=numrules, fill=numrules)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")

ggplot(num_viruses, aes(x=toolcombo2, y=num_viruses, 
                                  color=numrules, fill=numrules)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")
ggplot(num_viruses, aes(x=numrules, y=num_viruses)) +
  geom_boxplot(aes(color=numrules)) +
  geom_point(aes(color=numrules, fill=numrules)) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Tools") +
  ylab("Num Viruses Predicted")
viral_scores_nozeros <- viral_scores[rowSums(viral_scores)>0,]
viral_scores_nozeros <- viral_scores_nozeros + 1
viral_scores_nozeros <- as.data.frame(viral_scores_nozeros)

colnames(viral_scores_nozeros) <- num_viruses$toolcombo2
library(phyloseq)
tooldata <- num_viruses

rownames(tooldata) <- tooldata$toolcombo2
physeq_pooled <- phyloseq(otu_table(viral_scores_nozeros, taxa_are_rows = T),
                                     sample_data(tooldata))
ordination <- phyloseq::ordinate(physeq =physeq_pooled, method = "PCoA", distance = "bray")
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw() +
  geom_label(label=tooldata$toolcombo)

phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw()

to do: try coloring above based on the F1 scores of the testing set on each combination

bray_dist <- phyloseq::distance(physeq_pooled, method="bray")
clusters <- hclust(dist(bray_dist))
plot(clusters)

myclusters <- cutree(clusters, h=1.1)
names(myclusters[myclusters==1])
names(myclusters[myclusters==2])
names(myclusters[myclusters==3])
names(myclusters[myclusters==4])
names(myclusters[myclusters==5])

myclusters_df <- tibble(combo=names(myclusters),
                            cluster_index=myclusters)

myclusters_df <- separate(myclusters_df, col=combo, into=c("CheckV", "DVF",
                                                            "Kaiju", "VIBRANT",
                                                            "VirSorter", "VirSorter2"),
                          sep=" ", remove = F)


tool_count <- as.data.frame(rbind(table(myclusters_df$CheckV, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$DVF, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$Kaiju, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VIBRANT, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter2, myclusters_df$cluster_index)[2,])
                    )

tool_count$method <- c("CheckV", "DVF", "Kaiju", "VIBRANT", "VirSorter", "VirSorter2")

tool_count <- melt(tool_count)

colnames(tool_count) <- c("tool", "cluster_index", "tool_count")
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

ggplot(tool_count, aes(x=cluster_index, y=tool_count,
                   fill=cluster_index,
                   color=cluster_index)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Cluster") +
  ylab("Number of Times in Cluster") + 
  facet_wrap(~tool, scales = "free")
 ggplot(viruses, aes(x=checkv_viral_genes, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Viral Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=percent_viral, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Percent Genes Viral") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=hallmark, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
 
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
viruses_false_positive <- viruses[viruses$confusion_matrix_high_precision=="false positive",]
viruses_false_negative <- viruses[viruses$confusion_matrix_high_precision=="false negative",]
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=checkv_length,
                   color=checkv_length,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive, aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="bacteria"], aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="fungi"], aes(x=hallmark, y=checkv_length,
                   fill=keep_score_high_precision,
                   color=keep_score_high_precision,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="protist"], aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_negative, aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_negative, aes(x=hallmark, y=checkv_length,
                   fill=keep_score_high_precision,
                   color=keep_score_high_precision,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()
table(viruses$hallmark[viruses$confusion_matrix_high_precision=="false positive"]>0)

table(viruses$percent_host[viruses$confusion_matrix_high_precision=="false positive"]<50)

Visualizing confusion matrix by number of tools

confusion_by_keep_score <- viruses %>% 
  select(contains("keep_score"), seqtype, Index) %>%
  pivot_longer(cols=contains("keep_score"), 
               names_to="pipeline_type",
               values_to="pipeline_value") %>%
  mutate(pipeline_type=sub("keep_score_", "", pipeline_type)) %>%
  count(seqtype, Index, pipeline_type, pipeline_value)
confusion_by_keep_score$confusion_matrix <- "true negative"
confusion_by_keep_score$confusion_matrix[confusion_by_keep_score$seqtype=="virus" & confusion_by_keep_score$pipeline_value<1] <- "false negative"
confusion_by_keep_score$confusion_matrix[confusion_by_keep_score$seqtype=="virus" & confusion_by_keep_score$pipeline_value>=1] <- "true positive"
confusion_by_keep_score$confusion_matrix[confusion_by_keep_score$seqtype!="virus" & confusion_by_keep_score$pipeline_value>=1] <- "false positive"
confusion_by_keep_score$keep_score_visualize <- confusion_by_keep_score$pipeline_value
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value>1] <- "> 1"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==1] <- "1"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==0.5] <- "0.5"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==0] <- "0"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==-0.5] <- "-0.5"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==-1] <- "-1"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value<=-1] <- "< -1"

confusion_by_keep_score$keep_score_visualize <- factor(confusion_by_keep_score$keep_score_visualize, 
                                       levels=c("< -1", "-1", "-0.5", "0", "0.5","1", "> 1"))
ggplot(confusion_by_keep_score, aes(x=confusion_matrix, y=n,
                   fill=keep_score_visualize, color=keep_score_visualize)) +
  geom_boxplot() +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16)
  ) +
  scale_color_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 1)) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 0.5)) +
  xlab("Index") +
  ylab("Sequence Count") +
  facet_wrap(~pipeline_type, scales = "free")
---
title: "Viral Sequence Sorting Tools Evaluation"
author: Bridget Hegarty, James Riddell
date: 07-22-2022
output: html_notebook
---
This Rmarkdown file assesses the output of CheckV, DeepVirFinder, Kaiju,
VIBRANT, VirSorter, and VirSorter2 on multiple training sets of microbial DNA, 
primarily from NCBI. Created from fungal, viral, bacterial, archeael, protist,
and plasmid DNA sequences

Please reach out to James Riddell (riddell.26@buckeyemail.osu.edu) or
Bridget Hegarty (beh53@case.edu) regarding any issues, or open an issue on github.

```{r setup-library}
library(ggplot2)
library(plyr)
library(reshape2)
library(viridis)
library(tidyr)
library(dplyr)
library(readr)
library(data.table)
library(pROC)
library("stringr")
```

Import the file that combines the results from each of the tools from running "combining_tool_output.Rmd":
```{r}
viruses <- read_tsv("../IntermediaryFiles/viral_tools_combined.tsv")
```

This section defines a viralness score "keep_score" based on the tool classifications. 
A final keep_score above 1 indicates we will keep that sequence and call it viral.

VIBRANT
    Quality == "High Quality Draft": +1
    Quality == "Medium Quality Draft": +1
    Quality == "Low Quality Draft" & provirus: +0.5

Virsorter2
    Viral >= 50: +0.5
    Viral >= 0.95: +0.5
    RNA >= 0.9: +1
    lavidaviridae >= 0.9: +1
    NCLDV >= 0.9: +1

Virsorter
    category ==  1,4: +1
    category == 2,5: +0.5

DeepVirFinder:
    Score >= 0.7: +0.5

Tuning - No Viral Signature:
    Kaiju_viral = "cellular organisms": -0.5
    If host_genes >50 and NOT provirus: -1 
    If viral_genes == 0 and host_genes >= 1: -1
    If 3*viral_genes <= host_genes and NOT provirus: -1
    If length > 50,000 and hallmark <=1: -1
    If length < 5000 and checkv completeness <= 75: -0.5

Tuning - Viral Signature:
    Kaiju_viral = "Viruses": +0.5
    If %unknown >= 75 and length < 50000: +0.5
    If %viral >= 50: +0.5
    Hallmark > 2: +0.5
    

This script produces visualizations of these combined viral scorings and
includes ecological metrics like alpha diversity.

You can decide which combination is appropriate for them and only need use the
tools appropriate for your data.

```{r getting_viral_set_1}
getting_viral_set_1 <- function(input_seqs,
                                include_vibrant=FALSE, 
                                include_virsorter2=FALSE,
                                include_deepvirfinder=FALSE,
                                include_tuning_viral=FALSE,
                                include_tuning_not_viral=FALSE,
                                include_virsorter=FALSE) {
  
  keep_score <- rep(0, nrow(input_seqs))
  
  if (include_vibrant) {
    keep_score[input_seqs$vibrant_quality=="high quality draft"] <- keep_score[input_seqs$vibrant_quality=="high quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="medium quality draft"] <- keep_score[input_seqs$vibrant_quality=="medium quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$provirus] <- keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$provirus] + 0.5
  }
  
  if (include_virsorter2) {
    #keep_score[input_seqs$viral>=50 | input_seqs$lavidaviridae>=0.95 | input_seqs$NCLDV>=0.95] <- keep_score[input_seqs$viral>=50 | input_seqs$lavidaviridae>=0.95 | input_seqs$NCLDV>=0.95] + 0.5
    keep_score[input_seqs$max_score>=50] <- keep_score[input_seqs$max_score>=50] + 0.5
    keep_score[input_seqs$max_score>=95] <- keep_score[input_seqs$max_score>=95] + 0.5
    #keep_score[input_seqs$RNA>=0.95] <- keep_score[input_seqs$RNA>=0.95] + 1
  }
  
  if (include_virsorter) {
    keep_score[input_seqs$category==1] <- keep_score[input_seqs$category==1] + 1
    keep_score[input_seqs$category==2] <- keep_score[input_seqs$category==2] + 0.5
    keep_score[input_seqs$category==4] <- keep_score[input_seqs$category==4] + 1
    keep_score[input_seqs$category==5] <- keep_score[input_seqs$category==5] + 0.5
  }
  
  if (include_deepvirfinder) {
    keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] + 0.5
    keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] + 0.5
  }
  
  if (include_tuning_viral) {
    keep_score[input_seqs$Kaiju_Viral=="Viruses"] <- keep_score[input_seqs$Kaiju_Viral=="Viruses"] + 0.5
    keep_score[input_seqs$hallmark>2] <- keep_score[input_seqs$hallmark>2] + 0.5
    keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] <- keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] + 0.5
    keep_score[input_seqs$viral>=50] <- keep_score[input_seqs$viral>=50] + 0.5
  }
  
  if (include_tuning_not_viral) {
    keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] <- keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] - 0.5
    keep_score[input_seqs$checkv_host_genes>50 & !input_seqs$provirus] <- keep_score[input_seqs$checkv_host_genes>50 & !input_seqs$provirus] - 1
    keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] <- keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] - 1
    keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & !input_seqs$provirus] <- keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & !input_seqs$provirus] - 1 
    keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] - 1
    keep_score[(input_seqs$checkv_completeness<=75 | input_seqs$vibrant_quality=="complete circular")& input_seqs$checkv_length<=5000] <- keep_score[input_seqs$checkv_completeness<=75 & input_seqs$checkv_length<=5000] - 0.5 
  }
  
  return(keep_score)
  
}
```

# Assessing performance against the "truth"
note that this is only as accurate as the annotations of the input sequences

this function calculates the precision, recall, and F1 score for each pipeline
```{r}
assess_performance <- function(seqtype, keep_score) {
  
  truepositive <- rep("not viral", length(seqtype))
  truepositive[seqtype=="virus"] <- "viral"
  
  #make confusion matrix
  confusion_matrix <- rep("true negative", length(keep_score))
  confusion_matrix[truepositive=="viral" & keep_score<=1] <- "false negative"
  confusion_matrix[truepositive=="viral" & keep_score>=1] <- "true positive"
  confusion_matrix[truepositive=="not viral" & keep_score>=1] <- "false positive"
  
  TP <- table(confusion_matrix)[4]
  FP <- table(confusion_matrix)[2]
  TN <- table(confusion_matrix)[3]
  FN <- table(confusion_matrix)[1]
  
  precision <- TP/(TP+FP)
  recall <- TP/(TP+FN)
  F1 <- 2*precision*recall/(precision+recall)
  
  MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
  
  auc <- round(auc(truepositive, keep_score),4)
  
  #by type metrics
  fungal_FP <- table(confusion_matrix[seqtype=="fungi"])[2]
  protist_FP <- table(confusion_matrix[seqtype=="protist"])[2]
  bacterial_FP <- table(confusion_matrix[seqtype=="bacteria"])[2]
  viral_FN <- table(confusion_matrix[seqtype=="virus"])[1]
  
  performance <- c(precision, recall, F1, MCC, auc, fungal_FP, 
                   protist_FP, bacterial_FP, viral_FN)
  names(performance) <- c("precision", "recall", "F1", "MCC", "AUC", "fungal_FP",
                          "protist_FP", "bacterial_FP", "viral_FN")
  
  return(performance)
}
```

combination of tools list
```{r}
combos_list <- data.frame(toolcombo=rep(0, 64),
                          tune_not_viral=rep(0, 64),
                          DVF=rep(0, 64),
                          tune_viral=rep(0, 64),
                          VIBRANT=rep(0, 64),
                          VS=rep(0, 64),
                          VS2=rep(0, 64))
p <- 1

for (i in c(0,1)){
  for (j in c(0,1)){
    for (k in c(0,1)){
      for (l in c(0,1)){
        for (m in c(0,1)){
          for (n in c(0,1)){
            combos_list$toolcombo[p] <- paste(i,j,k,l,m,n)
            combos_list$toolcombo2[p] <- paste(if(i){"tv"}else{"0"},if(j){"DVF"}else{"0"},
                                               if(k){"tnv"}else{"0"},if(l){"VB"}else{"0"},
                                               if(m){"VS"}else{"0"},if(n){"VS2"}else{"0"})
            combos_list$tune_not_viral[p] <- i
            combos_list$DVF[p] <- j
            combos_list$tune_viral[p] <- k
            combos_list$VIBRANT[p] <- l
            combos_list$VS[p] <- m
            combos_list$VS2[p] <- n
            p <- p+1
          }
        }
      }
    }
  }
}

combos_list <- combos_list[-1,]
```

this function builds a list of all of the combinations that the user wants to 
test. 
In this case, we're comparing the performance of all unique combinations of the 
six tools.
```{r}
build_score_list <- function(input_seqs, combos) {
  output <- data.frame(precision=rep(0, nrow(combos)),
                       recall=rep(0, nrow(combos)),
                       F1=rep(0, nrow(combos)),
                       MCC=rep(0, nrow(combos)),
                       AUC=rep(0, nrow(combos)),
                       fungal_FP=rep(0, nrow(combos)),
                       protist_FP=rep(0, nrow(combos)),
                       bacterial_FP=rep(0, nrow(combos)),
                       viral_FN=rep(0, nrow(combos)))
  for (i in 1:nrow(combos)) {
    keep_score <- getting_viral_set_1(input_seqs, include_vibrant = combos$VIBRANT[i],
                                            include_virsorter = combos$VS[i],
                                            include_virsorter2 = combos$VS2[i],
                                            include_tuning_viral = combos$tune_viral[i],
                                            include_tuning_not_viral = combos$tune_not_viral[i],
                                            include_deepvirfinder = combos$DVF[i])
  
    output[i,1:9] <- assess_performance(input_seqs$seqtype, keep_score)
    
    output$toolcombo[i] <- paste(combos$tune_viral[i],combos$DVF[i],
                                 combos$tune_not_viral[i], combos$VIBRANT[i],
                                 combos$VS[i], combos$VS2[i])
  }
  
  output[is.na(output)] <- 0

  return (output)
}
```

## Calculate the performance of each pipeline
```{r}
accuracy_scores <- data.frame(testing_set_index=rep(0, nrow(combos_list)*10),
                      precision=rep(0, nrow(combos_list)*10),
                       recall=rep(0, nrow(combos_list)*10),
                       F1=rep(0, nrow(combos_list)*10),
                       MCC=rep(0, nrow(combos_list)*10), 
                      AUC=rep(0, nrow(combos_list)*10),
                      fungal_FP=rep(0, nrow(combos_list)*10),
                      protist_FP=rep(0, nrow(combos_list)*10),
                      bacterial_FP=rep(0, nrow(combos_list)*10),
                      viral_FN=rep(0, nrow(combos_list)*10))

accuracy_scores <- cbind(testing_set_index=rep(1, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==1,], combos_list))
for (i in 2:10) {
  accuracy_scores <- rbind(accuracy_scores,
                           cbind(testing_set_index=rep(i, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==i,], combos_list)))
}
```

```{r}
accuracy_scores$numrules <- str_count(accuracy_scores$toolcombo, "1")
#accuracy_scores <- accuracy_scores[order(accuracy_scores$numrules, decreasing=F),]
accuracy_scores <- accuracy_scores[order(accuracy_scores$MCC, decreasing=F),]
accuracy_scores$toolcombo <- factor(accuracy_scores$toolcombo, levels = unique(accuracy_scores$toolcombo))
accuracy_scores$numrules <- as.factor(accuracy_scores$numrules)
```

```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```


## Visualize how the precision, recall, and F1 scores change across pipelines.
```{r}
p2 <- ggplot(accuracy_scores, aes(x=toolcombo, y=F1, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("F1 Score")
p2

ggplot(accuracy_scores, aes(x=toolcombo, y=precision, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Precision")
ggplot(accuracy_scores, aes(x=toolcombo, y=recall, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Recall")

ggplot(accuracy_scores, aes(x=precision, y=recall, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=20),
  ) +
  xlab("Precision") +
  ylab("Recall") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1))

ggplot(accuracy_scores[accuracy_scores$testing_set_index==1,], aes(x=precision, y=recall)) +
  geom_label(alpha=0.7, label=accuracy_scores$toolcombo[accuracy_scores$testing_set_index==1]) +
  geom_point(alpha=0.5, aes(color=numrules, fill=numrules)) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Precision") +
  ylab("Recall")

ggplot(accuracy_scores, aes(x=toolcombo, y=abs(precision-recall), 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Precision-Recall")
ggplot(accuracy_scores, aes(x=toolcombo, y=MCC, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=20),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("MCC") +
  scale_fill_manual(name="Number of Rule Sets",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="Number of Rule Sets",
                     values = alpha(rev(pal(6)), 1))
  
ggplot(accuracy_scores, aes(x=toolcombo, y=AUC, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("AUC")
ggplot(accuracy_scores, aes(x=toolcombo, y=fungal_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Fungal False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=protist_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Protist False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=bacterial_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Bacterial False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=viral_FN, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Viral False Negatives")

ggplot(accuracy_scores, aes(x=toolcombo, y=viral_FN, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Viral False Negatives")

ggplot(accuracy_scores, aes(x=protist_FP, y=fungal_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Protist FP") +
  ylab("Fungal FP")

ggplot(accuracy_scores, aes(x=recall, y=fungal_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Recall") +
  ylab("Fungal FP")

ggplot(accuracy_scores, aes(x=recall, y=protist_FP, 
                                  color=numrules, fill=numrules)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Recall") +
  ylab("Protist FP")
```

```{r}
accuracy_scores_melt <- accuracy_scores %>% 
  select(testing_set_index, precision, recall, MCC, numrules, toolcombo) %>%
  pivot_longer(cols=c(precision, recall, MCC), 
               names_to="performance_metric",
               values_to="performance_metric_score")
```

```{r}
ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric=="MCC",], aes(x=numrules, y=performance_metric_score, 
                                  color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Number of Rule Sets") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) 
  
```

```{r}
ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric=="recall",], aes(x=numrules, y=performance_metric_score, 
                                  color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("recall") +
  xlab("Number of Rule Sets") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) 
  
```

```{r}
ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric!="MCC",], aes(x=numrules, y=performance_metric_score, 
                                  color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("Score") +
  xlab("Number of Rule Sets") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  facet_wrap(~performance_metric)
  
```
comparing metric with and without tuning rules
```{r}
accuracy_scores_melt$tuning_inc <- "no"
accuracy_scores_melt$tuning_inc[substring(accuracy_scores_melt$toolcombo, 1, 1)==1] <- "tv"
accuracy_scores_melt$tuning_inc[substring(accuracy_scores_melt$toolcombo, 3, 3)==1] <- "tnv"
accuracy_scores_melt$tuning_inc[substring(accuracy_scores_melt$toolcombo, 1, 1)==1 &
                                  substring(accuracy_scores_melt$toolcombo, 3, 3)==1] <- "both"
```

```{r}
ggplot(accuracy_scores_melt, aes(x=tuning_inc, y=performance_metric_score)) +
  geom_boxplot() +
  geom_point(aes(color=numrules, fill=numrules), alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Number of Tools") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  facet_wrap(~performance_metric)

ggplot(accuracy_scores_melt, aes(x=tuning_inc, y=performance_metric_score,
                                 color=numrules, fill=numrules)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Number of Tools") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  facet_wrap(~performance_metric)

ggplot(accuracy_scores_melt[accuracy_scores_melt$performance_metric!="MCC",], aes(x=tuning_inc, y=performance_metric_score)) +
  geom_boxplot() +
  geom_boxplot(aes(color=numrules, fill=numrules)) +
 # geom_point(aes(color=numrules, fill=numrules), alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("Score") +
  xlab("") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.3)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 0.7)) +
  facet_wrap(~performance_metric)
  
```

```{r}
write_tsv(accuracy_scores, "20221029_accuracy_scores.tsv")
```

to do: add in clustering and ordination like in the drinking water R notebook

# Experimenting

## high precision example
```{r}
viruses$keep_score_high_precision <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = T,
                                              include_virsorter = F)
```


```{r}
viruses$confusion_matrix_high_precision <- "true negative"
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision<1] <- "false negative"
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision>=1] <- "true positive"
viruses$confusion_matrix_high_precision[viruses$seqtype!="virus" & viruses$keep_score_high_precision>=1] <- "false positive"
```

visualizing confusion matrix by taxa
```{r}
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
```

```{r}
length(grep("true", viruses$confusion_matrix_high_precision))/nrow(viruses)
```

```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
ggplot(confusion_by_taxa, aes(y=count, x=confusion_matrix,
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free")
```
this rule set had the highest precision, but as you can see, this comes with a big sacrifice in recall



## high MCC example
```{r}
viruses$keep_score_high_MCC <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)
```


```{r}
viruses$confusion_matrix_high_MCC <- "true negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC<1] <- "false negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC>=1] <- "true positive"
viruses$confusion_matrix_high_MCC[viruses$seqtype!="virus" & viruses$keep_score_high_MCC>=1] <- "false positive"
```

accuracy:
```{r}
length(grep("true", viruses$confusion_matrix_high_MCC))/nrow(viruses)
```

recall
```{r}
length(grep("true positive", viruses$confusion_matrix_high_MCC))/length(grep("virus", viruses$seqtype))
```

```{r}
TP <- table(viruses$confusion_matrix_high_MCC)[4]
FP <- table(viruses$confusion_matrix_high_MCC)[2]
TN <- table(viruses$confusion_matrix_high_MCC)[3]
FN <- table(viruses$confusion_matrix_high_MCC)[1]

precision <- as.numeric(TP/(TP+FP))
precision
recall <- as.numeric(TP/(TP+FN))
recall
F1 <- as.numeric(2*precision*recall/(precision+recall))
F1

MCC <- as.numeric((TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN)))
MCC
```

precision=69%, recall=87%, MCC=77%

precision adjusting size to be equal viral/not viral
```{r}
TP <- table(viruses$confusion_matrix_high_MCC)[4]
FP <- table(viruses$confusion_matrix_high_MCC)[2]*.11
TN <- table(viruses$confusion_matrix_high_MCC)[3]*.11
FN <- table(viruses$confusion_matrix_high_MCC)[1]

precision <- as.numeric(TP/(TP+FP))
precision
recall <- as.numeric(TP/(TP+FN))
recall
F1 <- as.numeric(2*precision*recall/(precision+recall))
F1

MCC <- as.numeric((TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN)))
MCC
```

precision=0.95, recall=0.87, F1=0.91, MCC=0.82





visualizing confusion matrix by taxa
```{r}
confusion_by_taxa <- viruses %>% count(confusion_matrix_high_MCC, seqtype, Index)

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","index", "count")
```


```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
```


differences based on genome size
```{r}
viruses$size_class <- "3-5kb"
viruses$size_class[viruses$checkv_length>5000] <- "5-10kb"
viruses$size_class[viruses$checkv_length>10000] <- ">10kb"
```

```{r}
confusion_by_taxa <- viruses %>% count(confusion_matrix_high_MCC, seqtype, size_class, Index)

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","size", "index", "count")
```

```{r}
confusion_vir_called <- confusion_by_taxa %>% filter(confusion_matrix=="true positive" | confusion_matrix=="false positive") 

type_count <- viruses %>% count(seqtype, size_class, Index)

confusion_vir_called$per_viral <- 0

for (i in c(1:nrow(confusion_vir_called))) {
  confusion_vir_called$per_viral[i] <- confusion_vir_called$count[i]/type_count$n[type_count$seqtype==confusion_vir_called$seqtype[i] & 
                                                                                    type_count$Index==confusion_vir_called$index[i] &
                                                                                    type_count$size_class==confusion_vir_called$size[i]]*100
}

confusion_vir_called <- confusion_vir_called %>% group_by(seqtype, size) %>%
  summarise(mean=mean(per_viral), 
            sd=sd(per_viral))

confusion_vir_called$size <- factor(confusion_vir_called$size,
                                    levels = c("3-5kb", "5-10kb", ">10kb"))
```

```{r}
ggplot(confusion_vir_called, aes(y=mean, x=size,
                   fill=seqtype,
                   color=seqtype)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,
                 position=position_dodge(.9)) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Length") +
  ylab("Sequences Called Viral (%)") 
```



```{r}
viruses$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf_vs2 <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf_vs2_vs <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses$keep_score_vb_dvf_vs2_vs_tv <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses$keep_score_vb_dvf_vs2_vs_tv_tnv <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)
```

```{r}
viruses$true_virus <- "not"
viruses$true_virus[viruses$seqtype=="virus"] <- "virus"

viruses_long_scores <- viruses %>% 
  select(contains("keep_score_vb"), size_class, true_virus) %>%
  pivot_longer(cols=contains("keep_score_"), 
               names_to="rule_combination",
               values_to="viral_score") %>% 
  mutate(viral_score=as.factor(round(viral_score))) %>%
  group_by(rule_combination, viral_score, size_class, true_virus) %>%
  summarise(n = n())

viruses_long_scores$size_class <- factor(viruses_long_scores$size_class,
                                    levels = c("3-5kb", "5-10kb", ">10kb"))
```

```{r}
viruses_long_scores_addition <- viruses_long_scores[(viruses_long_scores$true_virus=="virus" &  (viruses_long_scores$rule_combination!="keep_score_vb_dvf_vs2_vs_tv_tnv") & viruses_long_scores$viral_score!="0"),]
ggplot(viruses_long_scores_addition, aes(y=n, x=rule_combination,
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "Purples") +
  xlab("") +
  ylab("Number of Sequences") + 
  scale_x_discrete(labels=c("VB", "VB+DVF", "VB+DVF+VS2", "VB+DVF+VS2+VS",
                            "VB+DVF+VS2+VS+addition")) +
  facet_grid(~true_virus, scales = "free")
```

```{r}
ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  scale_x_discrete(labels=c("VB", "VB+DVF", "VB+DVF+VS2", "VB+DVF+VS2+VS",
                            "VB+DVF+VS2+VS+addition", "VB+DVF+VS2+VS+addition-removal")) +
  facet_grid(~true_virus, scales = "free")
```

```{r}
ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  facet_grid(size_class~true_virus, scales = "free")
```


# Considering how each method contributes to the final prediction
```{r}
viruses_high <- viruses[viruses$keep_score_vb_dvf_vs2_vs_tv>=1,] 
viruses_high_mod <- viruses_high %>% select(keep_score_vb,keep_score_vb_dvf, 
                                            keep_score_vb_dvf_vs2, keep_score_vb_dvf_vs2_vs, 
                                            keep_score_vb_dvf_vs2_vs_tv, keep_score_vb_dvf_vs2_vs_tv_tnv)
#viruses_high_mod <- apply(viruses_high_mod, c(1,2), function(x) {if (x >= 1) {x <- 1} else {x <- 0}})
viruses_high_mod <- as_tibble(viruses_high_mod)


```

```{r}
sm_m <- reshape2::melt(viruses_high_mod)
colnames(sm_m) <- c("method", "viral_score")
```

```{r}
sm_m <- sm_m[sm_m$viral_score>0,]

sm_m$score <- sm_m$viral_score

sm_m$score[sm_m$viral_score==0.5] <- "0.5"
sm_m$score[sm_m$viral_score>=1] <- "1"
sm_m$score[sm_m$viral_score>=2] <- "2"
sm_m$score[sm_m$viral_score>=3] <- "3"
sm_m$score[sm_m$viral_score>=4] <- "4"
sm_m$score[sm_m$viral_score>=5] <- "5"

sm_m$score <- factor(sm_m$score, 
                                       levels=c("0.5", "1", "2","3","4","5"))
```


```{r}
ggplot(sm_m, aes(x=method, y=score,
                   fill=score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 1)) +
  xlab("") +
  ylab("Number of Sequences") +
  coord_flip()
```
## Another way of visualizing between rule sets

```{r}
viruses_mcc_alluvial <- data.frame(seqtype=viruses$seqtype,
                                   keep_score_high_MCC=viruses$keep_score_high_MCC,
                                   confusion_matrix_high_MCC=viruses$confusion_matrix_high_MCC)


viruses_mcc_alluvial$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_dvf <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_vs <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = F,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses_mcc_alluvial$keep_score_vs2 <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = F,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_tv <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = F,
                                              include_virsorter2 = F,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_mcc_alluvial$keep_score_tnv <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = T,
                                              include_virsorter = F)
```

```{r}
viruses_mcc_alluvial %>%
  count(seqtype, keep_score_high_MCC) %>% 
  spread(key = keep_score_high_MCC, value=n)
```


```{r}
viruses_mcc_alluvial <- viruses_mcc_alluvial %>%
  count(seqtype, keep_score_dvf, keep_score_vb, keep_score_vs,
        keep_score_vs2, keep_score_tv, keep_score_tnv, keep_score_high_MCC) %>%
  mutate(high_mcc_viral_score=factor(round(keep_score_high_MCC)))
```

```{r}
ggplot(viruses_mcc_alluvial,
       aes(axis1 = keep_score_dvf, axis2 = keep_score_vb, 
           axis3 = keep_score_vs, axis4 = keep_score_vs2, 
           axis5 = keep_score_tv, axis6 = keep_score_tnv, 
           y=n)) +
  geom_alluvium(aes(fill=high_mcc_viral_score),
                width = 0, knot.pos = 0, reverse = FALSE) +
  geom_stratum(width = 1/5) +
  theme_bw() +
  geom_text(stat = "stratum", aes(label = after_stat(stratum)),
            reverse = FALSE) +
  theme(
        axis.text.x=element_text(size=14, angle = 90)
        ) +
  scale_x_continuous(breaks=c(1,2,3,4,5,6),
    labels=c("dvf", "kj", "vs", "vs2",
             "tv", "tnv")) +
  scale_fill_brewer(palette = "PuOr", ) +
  facet_wrap(~seqtype, scales="free_y") 
``` 






## Visualizing confusion matrix by number of tools


```{r}
viruses$keep_score_visualize <- viruses$keep_score_high_MCC
viruses$keep_score_visualize[viruses$keep_score_high_MCC>1] <- "> 1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==1] <- "1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==0.5] <- "0.5"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==0] <- "0"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==-0.5] <- "-0.5"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==-1] <- "-1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC<=-1] <- "< -1"

viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
                                       levels=c("< -1", "-1", "-0.5", "0", "0.5","1", "> 1"))
#viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
#                                       labels=c("≤ 0", "≤ 0", "≤ 0", "0.5","1", "> 1"))
```

```{r}
levels(factor(viruses$keep_score_visualize))
```

```{r}
ggplot(viruses, aes(x=as.factor(Index),
                   fill=keep_score_visualize, color=keep_score_visualize)) +
  geom_bar(stat="count", position="stack") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16)
  ) +
  scale_color_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 1)) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 0.5)) +
  xlab("Index") +
  ylab("Sequence Count") +
  facet_wrap(~confusion_matrix_high_MCC, scales = "free")

```

## clustering
```{r}
viral_scores <- matrix(data=0, nrow=nrow(viruses), ncol=nrow(combos_list))
num_viruses <- data.frame(toolcombo=rep(0, nrow(combos_list)),
                          num_viruses=rep(0, nrow(combos_list)))

for (i in 1:nrow(combos_list)) {
  viral_scores[,i] <- getting_viral_set_1(viruses, include_vibrant = combos_list$VIBRANT[i],
                                            include_virsorter = combos_list$VS[i],
                                            include_virsorter2 = combos_list$VS2[i],
                                            include_tuning_viral = combos_list$tune_viral[i],
                                            include_tuning_not_viral = combos_list$tune_not_viral[i],
                                            include_deepvirfinder = combos_list$DVF[i])
  
  if (max(viral_scores[,i])<=0) {
    num_viruses$num_viruses[i] <- 0
  }
  else {
    num_viruses$num_viruses[i] <- table(viral_scores[,i]>=1)[[2]]
  }
  
  num_viruses$toolcombo[i] <- combos_list$toolcombo[i]
  
  num_viruses$toolcombo2[i] <- combos_list$toolcombo2[i]
}

num_viruses$numrules <- str_count(num_viruses$toolcombo, "1")
num_viruses <- num_viruses[order(num_viruses$num_viruses, decreasing=F),]
num_viruses$toolcombo <- factor(num_viruses$toolcombo, levels = unique(num_viruses$toolcombo))
num_viruses$toolcombo2 <- factor(num_viruses$toolcombo2, levels = unique(num_viruses$toolcombo2))
num_viruses$numrules <- as.factor(num_viruses$numrules)
```



```{r}
viral_scores_nozeros <- viral_scores[rowSums(viral_scores)>0,]
viral_scores_nozeros <- viral_scores_nozeros + 1
viral_scores_nozeros <- as.data.frame(viral_scores_nozeros)

colnames(viral_scores_nozeros) <- num_viruses$toolcombo
```

```{r}
library(phyloseq)
```


```{r}
tooldata <- num_viruses

rownames(tooldata) <- tooldata$toolcombo
```

```{r}
physeq_pooled <- phyloseq(otu_table(viral_scores_nozeros, taxa_are_rows = T),
                                     sample_data(tooldata))
```

```{r}
ordination <- phyloseq::ordinate(physeq =physeq_pooled, method = "PCoA", distance = "bray")
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw() +
  geom_label(label=tooldata$toolcombo)

phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw()
```

```{r}
bray_dist <- phyloseq::distance(physeq_pooled, method="bray")
clusters <- hclust(dist(bray_dist))
plot(clusters)

myclusters <- cutree(clusters, h=0.8)
```


```{r}
#names(myclusters[myclusters==1])
#names(myclusters[myclusters==2])
#names(myclusters[myclusters==3])
#names(myclusters[myclusters==4])
#names(myclusters[myclusters==5])

myclusters_df <- tibble(combo=names(myclusters),
                            cluster_index=myclusters)

myclusters_df <- separate(myclusters_df, col=combo, into=c("tnv", "DVF",
                                                            "tv", "VB",
                                                            "VS", "VS2"),
                          sep=" ", remove = F)


tool_count <- as.data.frame(rbind(table(myclusters_df$tnv, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$DVF, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$tv, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VB, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VS, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VS2, myclusters_df$cluster_index)[2,])
                    )

tool_count <- data.frame(t(apply(tool_count, c(1), function(x) {x <- x/table(myclusters_df$cluster_index)})))



tool_count$method <- c("tnv", "DVF", "tv", "VB", "VS", "VS2")

tool_count <- melt(tool_count)

colnames(tool_count) <- c("tool", "cluster_index", "tool_count_norm")
```

```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

ggplot(tool_count, aes(x=tool, y=tool_count_norm,
                   fill=tool,
                   color=tool)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "none",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    #legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Tool") +
  ylab("Proportion of Times in Cluster") + 
  facet_wrap(~cluster_index, nrow=1)
```

```{r}
accuracy_scores_melt <- accuracy_scores %>% 
  select(precision, recall, MCC, numrules, toolcombo) %>%
  group_by(numrules, toolcombo) %>%
  summarise(precision=mean(precision),
            recall=mean(recall),
            MCC=mean(MCC)) %>%
  pivot_longer(cols=c(precision, recall, MCC), 
               names_to="performance_metric",
               values_to="performance_metric_score")
```


```{r}
myclusters_df <- inner_join(accuracy_scores_melt, myclusters_df, 
                            by=c("toolcombo"="combo"))

myclusters_df$cluster_index <- as.factor(myclusters_df$cluster_index)
```

```{r}
ggplot(myclusters_df, aes(x=cluster_index, y=performance_metric_score, 
                                  color=cluster_index, fill=cluster_index)) +
  geom_boxplot() +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  ylab("MCC") +
  xlab("Cluster") +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(9)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(9)), 1)) +
  facet_wrap(~performance_metric)
```

## all 6 tools example
```{r}
viruses$keep_score_all <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)
```


```{r}
viruses$confusion_matrix_all <- "true negative"
viruses$confusion_matrix_all[viruses$seqtype=="virus" & viruses$keep_score_all<1] <- "false negative"
viruses$confusion_matrix_all[viruses$seqtype=="virus" & viruses$keep_score_all>=1] <- "true positive"
viruses$confusion_matrix_all[viruses$seqtype!="virus" & viruses$keep_score_all>=1] <- "false positive"
```

```{r}
TP <- table(viruses$confusion_matrix_all)[4]
FP <- table(viruses$confusion_matrix_all)[2]
TN <- table(viruses$confusion_matrix_all)[3]
FN <- table(viruses$confusion_matrix_all)[1]

precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
F1 <- 2*precision*recall/(precision+recall)
F1

MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
MCC
```
precision=62%, recall=92%, MCC=73%


visualizing confusion matrix by taxa
```{r}
confusion_by_taxa <- melt(table(viruses$confusion_matrix_all, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
```

```{r}
table(viruses$confusion_matrix_all)

length(grep("true", viruses$confusion_matrix_all))/nrow(viruses)
```

```{r}
length(grep("true positive", viruses$confusion_matrix_all))/length(grep("virus", viruses$seqtype))
```

```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
```


## high recall example
```{r}
viruses$keep_score_high_recall <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)
```


```{r}
viruses$confusion_matrix_high_recall <- "true negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall<1] <- "false negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall>=1] <- "true positive"
viruses$confusion_matrix_high_recall[viruses$seqtype!="virus" & viruses$keep_score_high_recall>=1] <- "false positive"
```


visualizing confusion matrix by taxa
```{r}
confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_recall, viruses$seqtype, viruses$Index))
colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")
```



```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
p2 <- ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
p2
```

accuracy:
```{r}
length(grep("true", viruses$confusion_matrix_high_recall))/nrow(viruses)
```
0.887

recall
```{r}
length(grep("true positive", viruses$confusion_matrix_high_recall))/length(grep("virus", viruses$seqtype))
```
recover almost all of the viruses this way, but more protist contamination

0.960

```{r}
confusion_by_taxa <- viruses %>% count(confusion_matrix_high_recall, seqtype, size_class)

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","size", "count")
```



## few tools, high MCC example
```{r}
viruses$keep_score_few_tools <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = F,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = F)
```


```{r}
viruses$confusion_matrix_few_tools <- "true negative"
viruses$confusion_matrix_few_tools[viruses$seqtype=="virus" & viruses$keep_score_few_tools<1] <- "false negative"
viruses$confusion_matrix_few_tools[viruses$seqtype=="virus" & viruses$keep_score_few_tools>=1] <- "true positive"
viruses$confusion_matrix_few_tools[viruses$seqtype!="virus" & viruses$keep_score_few_tools>=1] <- "false positive"
```

```{r}
TP <- table(viruses$confusion_matrix_few_tools)[4]
FP <- table(viruses$confusion_matrix_few_tools)[2]
TN <- table(viruses$confusion_matrix_few_tools)[3]
FN <- table(viruses$confusion_matrix_few_tools)[1]

precision <- TP/(TP+FP)
precision
recall <- TP/(TP+FN)
recall
F1 <- 2*precision*recall/(precision+recall)
F1

MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
MCC
```
precision=77%, recall=76%, MCC=74%


## Visualizing the different sets

```{r}
confusion_by_taxa_method <- viruses %>% 
  select(contains("confusion_matrix"), seqtype, Index) %>%
  pivot_longer(cols=contains("confusion_matrix"), 
               names_to="confusion_matrix_type",
               values_to="confusion_matrix_value") %>%
  count(seqtype, Index, confusion_matrix_type, confusion_matrix_value)
```

```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")
```

```{r}
ggplot(confusion_by_taxa_method, aes(y=n, x=confusion_matrix_type,
                   fill=confusion_matrix_value,
                   color=confusion_matrix_value)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free")
```

```{r}
confusion_by_taxa_method <- viruses %>% 
  select(contains("confusion_matrix"), seqtype, Index) %>%
  pivot_longer(cols=contains("confusion_matrix"), 
               names_to="confusion_matrix_type",
               values_to="confusion_matrix_value") %>%
  count(seqtype, Index, confusion_matrix_type, confusion_matrix_value) %>%
  filter(grepl("true", confusion_matrix_value)) %>%
  mutate(confusion_matrix_type=sub("confusion_matrix_", "", confusion_matrix_type))
```

```{r}
ggplot(confusion_by_taxa_method, aes(y=n, x=confusion_matrix_type,
                   fill=confusion_matrix_value,
                   color=confusion_matrix_value)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=10, angle=90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4))[3:4], 0.5),
                    labels=c( 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4))[3:4], 1),
                    labels=c(
                             "true negative", "true positive")) +
  xlab("Tool Set") +
  ylab("Contig Count") + 
  facet_wrap(~seqtype, scales = "free")
```

## another way of visualizing the different tool sets scores
```{r}
viruses$true_virus <- "not"
viruses$true_virus[viruses$seqtype=="virus"] <- "virus"

viruses_long_scores <- viruses %>% 
  select(contains("keep_score_high"), contains("keep_score_all"), size_class, true_virus) %>%
  pivot_longer(cols=contains("keep_score_"), 
               names_to="rule_combination",
               values_to="viral_score") %>% 
  mutate(viral_score=as.factor(round(viral_score))) %>%
  group_by(rule_combination, viral_score, size_class, true_virus) %>%
  summarise(n = n())

viruses_long_scores$size_class <- factor(viruses_long_scores$size_class,
                                    levels = c("3-5kb", "5-10kb", ">10kb"))
```

```{r}
ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  facet_grid(~true_virus, scales = "free")
```

```{r}
ggplot(viruses_long_scores, aes(y=n, x=rule_combination,
                   fill=viral_score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_brewer(palette = "PuOr", ) +
  xlab("") +
  ylab("Number of Sequences") + 
  facet_grid(size_class~true_virus, scales = "free")
```



































######################################################################
Extra Stuff
#####################################################################

```{r}
ggplot(viruses, aes(x=checkv_length, y=keep_score_high_MCC,
                   fill=confusion_matrix_high_MCC,
                   color=confusion_matrix_high_MCC)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
```


```{r}
ggplot(viruses, aes(x=checkv_completeness, y=hallmark,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Number of Hallmark Genes") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
```

```{r}
ggplot(viruses, aes(x=checkv_completeness, y=keep_score_high_MCC,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()
```

```{r}
ggplot(viruses, aes(x=confusion_matrix_high_recall, y=checkv_length,
                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") +
  scale_y_log10()
```



looking at false negatives
```{r}
viruses_false_negs <- viruses[(viruses$seqtype=="virus" & viruses$keep_score_high_recall<1),]
```

looking at protists calling viral
```{r}
viruses_false_pos_protists <- viruses[(viruses$seqtype=="protist" & viruses$keep_score_high_recall>=1),]
```











# Considering how each method contributes to the final prediction (high MCC)

```{r}
viruses$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_tv <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)
```

```{r}
viruses_high <- viruses[viruses$keep_score_vb_tv>=1,] #uncomment this line if want to use all 6 tools
viruses_high_mod <- viruses_high %>% select(keep_score_vb, 
                                            keep_score_vb_tv)
#viruses_high_mod <- apply(viruses_high_mod, c(1,2), function(x) {if (x >= 1) {x <- 1} else {x <- 0}})
viruses_high_mod <- as_tibble(viruses_high_mod)


```

```{r}
sm_m <- reshape2::melt(viruses_high_mod)
colnames(sm_m) <- c("method", "score")
```

```{r}
ggplot(sm_m, aes(x=method, y=score,
                   fill=as.factor(score))) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_manual(name = 'Number of Methods',
                     values = alpha(c(viridis(14)), 1)) +
  xlab("Primary Method") +
  ylab("Count of Viral Contigs") +
  coord_flip()
```




# ROC 
```{r}
library(pROC)
```

```{r}
viruses$truepositive <- rep(0, nrow(viruses))
viruses$truepositive[viruses$seqtype=="virus"] <- 1
```


```{r}
rocobj <- roc(viruses$truepositive, viruses$keep_score)
rocobj_all <- roc(viruses$truepositive, viruses$keep_score_all)
auc <- round(auc(viruses$truepositive, viruses$keep_score),4)
auc_all <- round(auc(viruses$truepositive, viruses$keep_score_all),4)
#create ROC plot
ggroc(rocobj, colour = 'steelblue', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc, ')')) +
  coord_equal()
ggroc(rocobj_all, colour = 'green', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc_all, ')'))
```
Sensitivity: The probability that the model predicts a positive outcome for an observation when indeed the outcome is positive.
Specificity: The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative.




# Comparing behavior of all testing sets combined (clustering analyses)

```{r}
viral_scores <- matrix(data=0, nrow=nrow(viruses), ncol=nrow(combos_list))
num_viruses <- data.frame(toolcombo=rep(0, nrow(combos_list)),
                          num_viruses=rep(0, nrow(combos_list)))

for (i in 1:nrow(combos_list)) {
  viral_scores[,i] <- getting_viral_set_1(viruses, include_vibrant = combos_list$VIBRANT[i],
                                            include_virsorter = combos_list$VS[i],
                                            include_virsorter2 = combos_list$VS2[i],
                                            include_tuning = combos_list$CheckV[i],
                                            include_kaiju = combos_list$Kaiju[i],
                                            include_deepvirfinder = combos_list$DVF[i])
  
  num_viruses$num_viruses[i] <- table(viral_scores[,i]>=1)[[2]]
  
  num_viruses$toolcombo[i] <- combos_list$toolcombo[i]
  
  num_viruses$toolcombo2[i] <- combos_list$toolcombo2[i]
}

num_viruses$numrules <- str_count(num_viruses$toolcombo, "1")
num_viruses <- num_viruses[order(num_viruses$num_viruses, decreasing=F),]
num_viruses$toolcombo <- factor(num_viruses$toolcombo, levels = unique(num_viruses$toolcombo))
num_viruses$toolcombo2 <- factor(num_viruses$toolcombo2, levels = unique(num_viruses$toolcombo2))
num_viruses$numrules <- as.factor(num_viruses$numrules)
```


```{r}
ggplot(num_viruses, aes(x=toolcombo, y=num_viruses, 
                                  color=numrules, fill=numrules)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")

ggplot(num_viruses, aes(x=toolcombo2, y=num_viruses, 
                                  color=numrules, fill=numrules)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")
```

```{r}
ggplot(num_viruses, aes(x=numrules, y=num_viruses)) +
  geom_boxplot(aes(color=numrules)) +
  geom_point(aes(color=numrules, fill=numrules)) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Tools") +
  ylab("Num Viruses Predicted")
```


```{r}
viral_scores_nozeros <- viral_scores[rowSums(viral_scores)>0,]
viral_scores_nozeros <- viral_scores_nozeros + 1
viral_scores_nozeros <- as.data.frame(viral_scores_nozeros)

colnames(viral_scores_nozeros) <- num_viruses$toolcombo2
```

```{r}
library(phyloseq)
```


```{r}
tooldata <- num_viruses

rownames(tooldata) <- tooldata$toolcombo2
```

```{r}
physeq_pooled <- phyloseq(otu_table(viral_scores_nozeros, taxa_are_rows = T),
                                     sample_data(tooldata))
```

```{r}
ordination <- phyloseq::ordinate(physeq =physeq_pooled, method = "PCoA", distance = "bray")
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw() +
  geom_label(label=tooldata$toolcombo)

phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numrules", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw()
```
to do: try coloring above based on the F1 scores of the testing set on each combination

```{r}
bray_dist <- phyloseq::distance(physeq_pooled, method="bray")
clusters <- hclust(dist(bray_dist))
plot(clusters)

myclusters <- cutree(clusters, h=1.1)
```


```{r}
names(myclusters[myclusters==1])
names(myclusters[myclusters==2])
names(myclusters[myclusters==3])
names(myclusters[myclusters==4])
names(myclusters[myclusters==5])

myclusters_df <- tibble(combo=names(myclusters),
                            cluster_index=myclusters)

myclusters_df <- separate(myclusters_df, col=combo, into=c("CheckV", "DVF",
                                                            "Kaiju", "VIBRANT",
                                                            "VirSorter", "VirSorter2"),
                          sep=" ", remove = F)


tool_count <- as.data.frame(rbind(table(myclusters_df$CheckV, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$DVF, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$Kaiju, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VIBRANT, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter2, myclusters_df$cluster_index)[2,])
                    )

tool_count$method <- c("CheckV", "DVF", "Kaiju", "VIBRANT", "VirSorter", "VirSorter2")

tool_count <- melt(tool_count)

colnames(tool_count) <- c("tool", "cluster_index", "tool_count")
```

```{r}
pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

ggplot(tool_count, aes(x=cluster_index, y=tool_count,
                   fill=cluster_index,
                   color=cluster_index)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Cluster") +
  ylab("Number of Times in Cluster") + 
  facet_wrap(~tool, scales = "free")
```


```{r}
 ggplot(viruses, aes(x=checkv_viral_genes, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Viral Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=percent_viral, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Percent Genes Viral") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=hallmark, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
 
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
```

```{r}
viruses_false_positive <- viruses[viruses$confusion_matrix_high_precision=="false positive",]
viruses_false_negative <- viruses[viruses$confusion_matrix_high_precision=="false negative",]
```

```{r}
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=checkv_length,
                   color=checkv_length,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive, aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="bacteria"], aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="fungi"], aes(x=hallmark, y=checkv_length,
                   fill=keep_score_high_precision,
                   color=keep_score_high_precision,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="protist"], aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_negative, aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

ggplot(viruses_false_negative, aes(x=hallmark, y=checkv_length,
                   fill=keep_score_high_precision,
                   color=keep_score_high_precision,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()
```




```{r}
table(viruses$hallmark[viruses$confusion_matrix_high_precision=="false positive"]>0)

table(viruses$percent_host[viruses$confusion_matrix_high_precision=="false positive"]<50)
```


## Visualizing confusion matrix by number of tools

```{r}
confusion_by_keep_score <- viruses %>% 
  select(contains("keep_score"), seqtype, Index) %>%
  pivot_longer(cols=contains("keep_score"), 
               names_to="pipeline_type",
               values_to="pipeline_value") %>%
  mutate(pipeline_type=sub("keep_score_", "", pipeline_type)) %>%
  count(seqtype, Index, pipeline_type, pipeline_value)
```

```{r}
confusion_by_keep_score$confusion_matrix <- "true negative"
confusion_by_keep_score$confusion_matrix[confusion_by_keep_score$seqtype=="virus" & confusion_by_keep_score$pipeline_value<1] <- "false negative"
confusion_by_keep_score$confusion_matrix[confusion_by_keep_score$seqtype=="virus" & confusion_by_keep_score$pipeline_value>=1] <- "true positive"
confusion_by_keep_score$confusion_matrix[confusion_by_keep_score$seqtype!="virus" & confusion_by_keep_score$pipeline_value>=1] <- "false positive"
```

```{r}
confusion_by_keep_score$keep_score_visualize <- confusion_by_keep_score$pipeline_value
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value>1] <- "> 1"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==1] <- "1"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==0.5] <- "0.5"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==0] <- "0"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==-0.5] <- "-0.5"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value==-1] <- "-1"
confusion_by_keep_score$keep_score_visualize[confusion_by_keep_score$pipeline_value<=-1] <- "< -1"

confusion_by_keep_score$keep_score_visualize <- factor(confusion_by_keep_score$keep_score_visualize, 
                                       levels=c("< -1", "-1", "-0.5", "0", "0.5","1", "> 1"))
```

```{r}
ggplot(confusion_by_keep_score, aes(x=confusion_matrix, y=n,
                   fill=keep_score_visualize, color=keep_score_visualize)) +
  geom_boxplot() +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16)
  ) +
  scale_color_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 1)) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(viridis(6)), 0.5)) +
  xlab("Index") +
  ylab("Sequence Count") +
  facet_wrap(~pipeline_type, scales = "free")

```
